Relationship between land surface temperature and normalized difference water index on various land surfaces: A seasonal analysis

Yıl 2021, Cilt: 6 Sayı: 3, 165 - 173, 15.10.2021

Subhanil Guha Himanshu Govil

https://doi.org/10.26833/ijeg.821730

Cited By: 6

Öz

The present study examines the seasonal relationship between land surface temperature (LST) and normalized difference water index (NDWI) on various land surfaces in Raipur City of India by using a series of Landsat images for four specific seasons since 1991-92. The LST is retrieved using the mono-window algorithm technique. The results show that the LST of the study area is noticeably affected by surface composition. The best correlation (correlation coefficient r = 0.42) between the LST and NDWI is achieved in the post-monsoon season, followed by the monsoon season (r = 0.33), pre-monsoon season (r = 0.25), and winter season (r = 0.04). There is a moderate negative correlation (r = -0.49, -0.33, -0.31, and -0.25 in the pre-monsoon, monsoon, post-monsoon, and winter season, respectively) generated between the LST and NDWI on water bodies. On green vegetation, this LST-NDWI correlation is moderate positive (r = 0.67, 0.43, 0.50, and 0.25 in the pre-monsoon, monsoon, post-monsoon, and winter season, respectively). On human settlement and barren land surface, the correlation is weak positive (r = 0.24, 0.21, 0.27, and 0.15 in the pre-monsoon, monsoon, post-monsoon, and winter season, respectively). The output of the research work can be used in the town planning section of any urban agglomeration.

Anahtar Kelimeler

Landsat, Land surface, LST, NDWI, Raipur

Kaynakça

• Barsi J, Schott J, Hook S, Raqueno N, Markham B & Radocinski R (2014). Landsat-8 thermal infrared sensor (TIRS) vicarious radiometric calibration. Remote Sensing, 6(11), 11607-11626.
• Carlson T N & Ripley D A (1997). On the Relation between NDVI, Fractional Vegetation Cover, and Leaf Area Index. Remote Sensing of Environment, 62, 241-252. https://doi.org/10.1016/S0034-4257(97)00104-1
• Chen X L, Zhao H M, Li P X & Yi Z Y (2006). Remote sensing image-based analysis of the relationship between urban heat island and land use/cover changes. Remote Sensing of Environment, 104(2), 133–146. https://doi.org/10.1016/j.rse.2005.11.016
• Choudhury D, Das K, & Das A (2019). Assessment of land use land cover changes and its impact on variations of land surface temperature in Asansol-Durgapur Development Region. Egyptian Journal of Remote Sensing and Space Sciences, 22(2), 203-218. https://doi.org/10.1016/j.ejrs.2018.05.004
• Coll C, Galve J M, Sanchez J M & Caselles V. 2010. Validation of Landsat-7/ETM+ thermal-band calibration and atmospheric correction with ground-based measurements. IEEE Transactions on Geoscience and Remote Sensing, 48(1), 547–555. https://doi.org/10.1109/TGRS.2009.2024934
• Essa W, Verbeiren B, Van der Kwast J, Van de Voorde T & Batelaan O (2012). Evaluation of the DisTrad thermal sharpening methodology for urban areas. International Journal of Applied Earth Observation and Geoinformation, 19, 163-172. https://doi.org/10.1016/j.jag.2012.05.010
• Ghobadi Y., Pradhan B., Shafri H.Z.M. & Kabiri K. 2014. Assessment of spatial relationship between land surface temperature and land use/cover retrieval from multi-temporal remote sensing data in South Karkheh Sub-basin, Iran. Arabian Journal of Geosciences, 8(1), 525–537. https://doi: 10.1007/s12517-013-1244-3.
• Govil H, Guha S, Dey A & Gill N (2019). Seasonal evaluation of downscaled land surface temperature: A case study in a humid tropical city. Heliyon, 5(6), e01923. https://doi.org/ 10.1016/j.heliyon.2019.e01923
• Govil H, Guha S, Diwan P, Gill N & Dey A (2020). Analyzing Linear Relationships of LST with NDVI and MNDISI Using Various Resolution Levels of Landsat 8 OLI and TIRS Data. Data Management, Analytics and Innovation. Advances in Intelligent Systems and Computing, 1042. Springer, Singapore, 171-184. https://doi.org/10.1007/978-981-32-9949-8_13
• Guha S, Govil H & Besoya M (2020c). An investigation on seasonal variability between LST and NDWI in an urban environment using Landsat satellite data. Geomatics, Natural Hazards and Risk, 11(1), 1319-1345. https://doi.org/10.1080/19475705.2020.1789762
• Guha S, Govil H & Mukherjee S (2017). Dynamic analysis and ecological evaluation of urban heat islands in Raipur city, India. Journal of Applied Remote Sensing, 11(3), 036020. https://doi:10.1117/1.JRS.11.036020
• Guha S, Govil H, Dey A & Gill N (2020a). A case study on the relationship between land surface temperature and land surface indices in Raipur City, India. Geografisk Tidsskrift-Danish Journal of Geography, 120(1), 35-50. https://doi.org/10.1080/00167223.2020.1752272
• Guha S, Govil H, Gill N & Dey A (2020b). Analytical study on the relationship between land surface temperature and land use/land cover indices. Annals of GIS, 26(2), 201-216. https://doi.org/10.1080/19475683.2020.1754291
• Hao X, Li W & Deng H (2016). The oasis effect and summer temperature rise in arid regions-case study in Tarim Basin. Scientific Reports, 6, 35418. https://doi.org/10.1038/srep35418
• Hou G L, Zhang H Y, Wang Y Q, Qiao Z H & Zhang Z X (2010). Retrieval and Spatial Distribution of Land Surface Temperature in the Middle Part of Jilin Province Based on MODIS Data. Scientia Geographica sinica, 30, 421-427.
• Li J (2006). Estimating land surface temperature from Landsat-5 TM. Remote Sensing Technology and Application, 21, 322-326.
• Li W F, Cao Q W, Kun L, & Wu J S (2017). Linking potential heat source and sink to urban heat island: Heterogene-ous effects of landscape pattern on land surface temperature. Science of the Total Environment, 586, 457–465. https://doi.org/10.1016/j.scitotenv.2017.01.191
• Markham B L & Barker J K (1985). Spectral characteristics of the LANDSAT thematic mapper sensors. International Journal of Remote Sensing, 6(5), 697–716. https://doi.org/10.1080/01431168508948492
• McFeeters S K (1996). The use of the Normalized Difference Water Index (NDWI) in the delineation of open water features. International Journal of Remote Sensing, 17(7), 1425-1432. https://doi.org/10.1080/01431169608948714
• McFeeters S K (2013). Using the Normalized Difference Water Index (NDWI) within a Geographic Information System to Detect Swimming Pools for Mosquito Abatement: A Practical Approach. Remote Sensing, 5(7), 3544-3561. https://doi.org/10.3390/rs5073544
• Qin Z, Karnieli A & Barliner P (2001). A Mono-Window Algorithm for Retrieving Land Surface Temperature from Landsat TM Data and Its Application to the Israel-Egypt Border Region. International Journal of Remote Sensing, 22(18), 3719-3746. https://doi:10.1080/01431160010006971
• Sobrino J A, Jimenez-Munoz J C & Paolini L (2004). Land surface temperature retrieval from Landsat TM5. Remote Sensing of Environment, 9, 434–440. https://doi:10.1016/j.rse.2004.02.003
• Sobrino J A, Raissouni N & Li Z (2001). A comparative study of land surface emissivity retrieval from NOAA data. Remote Sensing of Environment, 75(2), 256–266. https://doi.org/10.1016/S0034-4257(00)00171-1
• Sun Q, Tan J & Xu Y (2010). An ERDAS image processing method for retrieving LST and describing urban heat evolution: A case study in the Pearl River Delta Region in South China. Environmental Earth Science, 59, 1047-1055.
• Tomlinson C J, Chapman L, Trones J E & Baker C (2011). Remote sensing land surface temperature for meteorology and climatology: a review. Meteorological Application, 118, 296–306. https://doi.org/10.1002/met.287
• URL-1: https://censusindia.gov.in/2011
• URL-2: http://www.surveyofindia.gov.in
• URL-3: https://www.earthexplorer.usgs.gov
• Vlassova L, Perez-Cabello F, Nieto H, Martín P, Riaño D, & De La Riva J (2014). Assessment of methods for land surface temperature retrieval from Landsat-5 TM images applicable to multiscale tree-grass ecosystem modeling. Remote Sensing, 6(5), 4345-4368.
• Wu C, Li J, Wang C, Song C, Chen Y, Finka M & Rosa D L (2019). Understanding the relationship between urban blue infrastructure and land surface temperature. Science of the Total Environment, 694, 133742. https://doi.org/10.1016/j.scitotenv.2019.133742
• Wukelic G E, Gibbons D E, Martucci L M & Foote H P (1989). Radiometric calibration of Landsat Thematic Mapper thermal band. Remote Sensing of Environment, 28, 339–347. https://doi.org/10.1016/0034-4257(89)90125-9
• Yang J & Qiu J (1996). The empirical expressions of the relation between precipitable water and ground water vapor pressure for some areas in China. Scientia Atmospherica Sinica, 20, 620-626.
• Yuan X, Wang W, Cui J, Meng F, Kurban A & De Maeyer P (2017). Vegetation changes and land surface feedbacks drive shifts in local temperatures over Central Asia. Scientific Reports, 7(1), 3287. https://doi.org/10.1038/s41598-017-03432-2
• Zanter K (2019). Landsat 8 (L8) Data Users Handbook; EROS: Sioux Falls, SD, USA.
• Zhang X, Estoque R C & Murayama Y (2017). An urban heat island study in Nanchang City, China based on land surface temperature and social-ecological variables. Sustainable Cities and Society, 32, 557-568. https://doi.org/10.1016/j.scs.2017.05.005